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<article id="content">
<header>
<h1 class="title">Module <code>silk.backbones.superpoint.vgg</code></h1>
</header>
<section id="section-intro">
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python"># Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.

# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.

from copy import deepcopy
from typing import Callable, Iterable, List, Union

import torch
from silk.backbones.silk.coords import (
    CoordinateMappingProvider,
    mapping_from_torch_module,
    Identity,
)


def vgg_block(
    in_channels: int,
    out_channels: int,
    kernel_size: int,
    use_batchnorm: bool = True,
    non_linearity: str = &#34;relu&#34;,
    padding: int = 1,
) -&gt; torch.nn.Module:
    &#34;&#34;&#34;
    The VGG block for the model.
    This block contains a 2D convolution, a ReLU activation, and a
    2D batch normalization layer.
    Args:
        in_channels (int): the number of input channels to the Conv2d layer
        out_channels (int): the number of output channels
        kernel_size (int): the size of the kernel for the Conv2d layer
        use_batchnorm (bool): whether or not to include a batchnorm layer.
            Default is true (batchnorm will be used).
    Returns:
        vgg_blk (nn.Sequential): the vgg block layer of the model
    &#34;&#34;&#34;

    if non_linearity == &#34;relu&#34;:
        non_linearity = torch.nn.ReLU(inplace=True)
    else:
        raise NotImplementedError

    # the paper states that batchnorm is used after each convolution layer
    if use_batchnorm:
        vgg_blk = torch.nn.Sequential(
            torch.nn.Conv2d(in_channels, out_channels, kernel_size, padding=padding),
            non_linearity,
            torch.nn.BatchNorm2d(out_channels),
        )
    # however, the official implementation does not include batchnorm
    else:
        vgg_blk = torch.nn.Sequential(
            torch.nn.Conv2d(in_channels, out_channels, kernel_size, padding=padding),
            non_linearity,
        )

    return vgg_blk


class VGG(torch.nn.Module, CoordinateMappingProvider):
    &#34;&#34;&#34;
    The VGG backbone.
    &#34;&#34;&#34;

    def __init__(
        self,
        num_channels: int = 1,
        use_batchnorm: bool = False,
        use_max_pooling: bool = True,
        padding: int = 1,
    ):
        &#34;&#34;&#34;
        Initialize the VGG backbone model.
        Can take an input image of any number of channels (e.g. grayscale, RGB).
        &#34;&#34;&#34;
        torch.nn.Module.__init__(self)
        CoordinateMappingProvider.__init__(self)

        assert padding in {0, 1}

        self.padding = padding
        self.use_max_pooling = use_max_pooling

        if use_max_pooling:
            self.mp = torch.nn.MaxPool2d(2, stride=2)
        else:
            self.mp = torch.nn.Identity()

        # convolution layers (encoder)
        self.l1 = torch.nn.Sequential(
            vgg_block(
                num_channels,
                64,
                3,
                use_batchnorm=use_batchnorm,
                padding=padding,
            ),
            vgg_block(
                64,
                64,
                3,
                use_batchnorm=use_batchnorm,
                padding=padding,
            ),
        )

        self.l2 = torch.nn.Sequential(
            vgg_block(
                64,
                64,
                3,
                use_batchnorm=use_batchnorm,
                padding=padding,
            ),
            vgg_block(
                64,
                64,
                3,
                use_batchnorm=use_batchnorm,
                padding=padding,
            ),
        )

        self.l3 = torch.nn.Sequential(
            vgg_block(
                64,
                128,
                3,
                use_batchnorm=use_batchnorm,
                padding=padding,
            ),
            vgg_block(
                128,
                128,
                3,
                use_batchnorm=use_batchnorm,
                padding=padding,
            ),
        )

        self.l4 = torch.nn.Sequential(
            vgg_block(
                128,
                128,
                3,
                use_batchnorm=use_batchnorm,
                padding=padding,
            ),
            vgg_block(
                128,
                128,
                3,
                use_batchnorm=use_batchnorm,
                padding=padding,
            ),
        )

    def mappings(self):
        mapping = Identity()
        mapping = mapping + mapping_from_torch_module(self.l1)
        mapping = mapping + mapping_from_torch_module(self.mp)
        mapping = mapping + mapping_from_torch_module(self.l2)
        mapping = mapping + mapping_from_torch_module(self.mp)
        mapping = mapping + mapping_from_torch_module(self.l3)
        mapping = mapping + mapping_from_torch_module(self.mp)
        mapping = mapping + mapping_from_torch_module(self.l4)

        return mapping

    def forward(self, images: torch.Tensor) -&gt; torch.Tensor:
        &#34;&#34;&#34;
        Goes through the layers of the VGG model as the forward pass.
        Computes the output.
        Args:
            images (tensor): image pytorch tensor with
                shape N x num_channels x H x W
        Returns:
            output (tensor): the output point pytorch tensor with
            shape N x cell_size^2+1 x H/8 x W/8.
        &#34;&#34;&#34;
        o1 = self.l1(images)
        o1 = self.mp(o1)

        o2 = self.l2(o1)
        o2 = self.mp(o2)

        o3 = self.l3(o2)
        o3 = self.mp(o3)

        # features
        o4 = self.l4(o3)

        return o4


def parametric_vgg_block(
    in_channels: int,
    out_channels: int,
    kernel_size: int,
    normalization_fn,
    non_linearity: str = &#34;relu&#34;,
    padding: int = 1,
) -&gt; torch.nn.Module:
    if non_linearity == &#34;relu&#34;:
        non_linearity = torch.nn.ReLU(inplace=True)
    else:
        raise NotImplementedError

    vgg_blk = torch.nn.Sequential(
        torch.nn.Conv2d(in_channels, out_channels, kernel_size, padding=padding),
        non_linearity,
        normalization_fn,
    )

    return vgg_blk


class ParametricVGG(torch.nn.Module, CoordinateMappingProvider):
    DEFAULT_NORMALIZATION_FN = torch.nn.Identity()

    def __init__(
        self,
        input_num_channels: int = 1,
        normalization_fn: Union[Callable, List[Callable]] = DEFAULT_NORMALIZATION_FN,
        use_max_pooling: bool = True,
        padding: int = 1,
        channels: List[int] = (64, 64, 128, 128),
    ):
        CoordinateMappingProvider.__init__(self)
        torch.nn.Module.__init__(self)

        assert padding in {0, 1}
        assert len(channels) &gt;= 1

        self.padding = padding
        self.use_max_pooling = use_max_pooling
        if isinstance(normalization_fn, Iterable):
            normalization_fn = tuple(normalization_fn)
            assert len(normalization_fn) == len(channels)
        else:
            normalization_fn = tuple([normalization_fn] * len(channels))

        if use_max_pooling:
            self.mp = torch.nn.MaxPool2d(2, stride=2)
        else:
            self.mp = torch.nn.Identity()

        self.layers = []
        self.channels = (input_num_channels,) + channels
        for i in range(1, len(self.channels)):
            layer = torch.nn.Sequential(
                parametric_vgg_block(
                    self.channels[i - 1],
                    self.channels[i],
                    3,
                    deepcopy(normalization_fn[i - 1]),
                    &#34;relu&#34;,
                    padding,
                ),
                parametric_vgg_block(
                    self.channels[i],
                    self.channels[i],
                    3,
                    deepcopy(normalization_fn[i - 1]),
                    &#34;relu&#34;,
                    padding,
                ),
            )
            self.layers.append(layer)
        self.layers = torch.nn.ModuleList(self.layers)

    def mappings(self):
        mapping = Identity()
        for layer in self.layers[:-1]:
            mapping = mapping + mapping_from_torch_module(layer)
            mapping = mapping + mapping_from_torch_module(self.mp)
        mapping = mapping + mapping_from_torch_module(self.layers[-1])

        return mapping

    def forward(self, images: torch.Tensor) -&gt; torch.Tensor:
        x = images
        for layer in self.layers[:-1]:
            x = layer(x)
            x = self.mp(x)
        x = self.layers[-1](x)
        return x</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-functions">Functions</h2>
<dl>
<dt id="silk.backbones.superpoint.vgg.parametric_vgg_block"><code class="name flex">
<span>def <span class="ident">parametric_vgg_block</span></span>(<span>in_channels: int, out_channels: int, kernel_size: int, normalization_fn, non_linearity: str = 'relu', padding: int = 1) ‑> torch.nn.modules.module.Module</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def parametric_vgg_block(
    in_channels: int,
    out_channels: int,
    kernel_size: int,
    normalization_fn,
    non_linearity: str = &#34;relu&#34;,
    padding: int = 1,
) -&gt; torch.nn.Module:
    if non_linearity == &#34;relu&#34;:
        non_linearity = torch.nn.ReLU(inplace=True)
    else:
        raise NotImplementedError

    vgg_blk = torch.nn.Sequential(
        torch.nn.Conv2d(in_channels, out_channels, kernel_size, padding=padding),
        non_linearity,
        normalization_fn,
    )

    return vgg_blk</code></pre>
</details>
</dd>
<dt id="silk.backbones.superpoint.vgg.vgg_block"><code class="name flex">
<span>def <span class="ident">vgg_block</span></span>(<span>in_channels: int, out_channels: int, kernel_size: int, use_batchnorm: bool = True, non_linearity: str = 'relu', padding: int = 1) ‑> torch.nn.modules.module.Module</span>
</code></dt>
<dd>
<div class="desc"><p>The VGG block for the model.
This block contains a 2D convolution, a ReLU activation, and a
2D batch normalization layer.</p>
<h2 id="args">Args</h2>
<dl>
<dt><strong><code>in_channels</code></strong> :&ensp;<code>int</code></dt>
<dd>the number of input channels to the Conv2d layer</dd>
<dt><strong><code>out_channels</code></strong> :&ensp;<code>int</code></dt>
<dd>the number of output channels</dd>
<dt><strong><code>kernel_size</code></strong> :&ensp;<code>int</code></dt>
<dd>the size of the kernel for the Conv2d layer</dd>
<dt><strong><code>use_batchnorm</code></strong> :&ensp;<code>bool</code></dt>
<dd>whether or not to include a batchnorm layer.
Default is true (batchnorm will be used).</dd>
</dl>
<h2 id="returns">Returns</h2>
<p>vgg_blk (nn.Sequential): the vgg block layer of the model</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def vgg_block(
    in_channels: int,
    out_channels: int,
    kernel_size: int,
    use_batchnorm: bool = True,
    non_linearity: str = &#34;relu&#34;,
    padding: int = 1,
) -&gt; torch.nn.Module:
    &#34;&#34;&#34;
    The VGG block for the model.
    This block contains a 2D convolution, a ReLU activation, and a
    2D batch normalization layer.
    Args:
        in_channels (int): the number of input channels to the Conv2d layer
        out_channels (int): the number of output channels
        kernel_size (int): the size of the kernel for the Conv2d layer
        use_batchnorm (bool): whether or not to include a batchnorm layer.
            Default is true (batchnorm will be used).
    Returns:
        vgg_blk (nn.Sequential): the vgg block layer of the model
    &#34;&#34;&#34;

    if non_linearity == &#34;relu&#34;:
        non_linearity = torch.nn.ReLU(inplace=True)
    else:
        raise NotImplementedError

    # the paper states that batchnorm is used after each convolution layer
    if use_batchnorm:
        vgg_blk = torch.nn.Sequential(
            torch.nn.Conv2d(in_channels, out_channels, kernel_size, padding=padding),
            non_linearity,
            torch.nn.BatchNorm2d(out_channels),
        )
    # however, the official implementation does not include batchnorm
    else:
        vgg_blk = torch.nn.Sequential(
            torch.nn.Conv2d(in_channels, out_channels, kernel_size, padding=padding),
            non_linearity,
        )

    return vgg_blk</code></pre>
</details>
</dd>
</dl>
</section>
<section>
<h2 class="section-title" id="header-classes">Classes</h2>
<dl>
<dt id="silk.backbones.superpoint.vgg.ParametricVGG"><code class="flex name class">
<span>class <span class="ident">ParametricVGG</span></span>
<span>(</span><span>input_num_channels: int = 1, normalization_fn: Union[Callable, List[Callable]] = Identity(), use_max_pooling: bool = True, padding: int = 1, channels: List[int] = (64, 64, 128, 128))</span>
</code></dt>
<dd>
<div class="desc"><p>Base class for all neural network modules.</p>
<p>Your models should also subclass this class.</p>
<p>Modules can also contain other Modules, allowing to nest them in
a tree structure. You can assign the submodules as regular attributes::</p>
<pre><code>import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))
</code></pre>
<p>Submodules assigned in this way will be registered, and will have their
parameters converted too when you call :meth:<code>to</code>, etc.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>As per the example above, an <code>__init__()</code> call to the parent class
must be made before assignment on the child.</p>
</div>
<p>:ivar training: Boolean represents whether this module is in training or
evaluation mode.
:vartype training: bool</p>
<p>Initializes internal Module state, shared by both nn.Module and ScriptModule.</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class ParametricVGG(torch.nn.Module, CoordinateMappingProvider):
    DEFAULT_NORMALIZATION_FN = torch.nn.Identity()

    def __init__(
        self,
        input_num_channels: int = 1,
        normalization_fn: Union[Callable, List[Callable]] = DEFAULT_NORMALIZATION_FN,
        use_max_pooling: bool = True,
        padding: int = 1,
        channels: List[int] = (64, 64, 128, 128),
    ):
        CoordinateMappingProvider.__init__(self)
        torch.nn.Module.__init__(self)

        assert padding in {0, 1}
        assert len(channels) &gt;= 1

        self.padding = padding
        self.use_max_pooling = use_max_pooling
        if isinstance(normalization_fn, Iterable):
            normalization_fn = tuple(normalization_fn)
            assert len(normalization_fn) == len(channels)
        else:
            normalization_fn = tuple([normalization_fn] * len(channels))

        if use_max_pooling:
            self.mp = torch.nn.MaxPool2d(2, stride=2)
        else:
            self.mp = torch.nn.Identity()

        self.layers = []
        self.channels = (input_num_channels,) + channels
        for i in range(1, len(self.channels)):
            layer = torch.nn.Sequential(
                parametric_vgg_block(
                    self.channels[i - 1],
                    self.channels[i],
                    3,
                    deepcopy(normalization_fn[i - 1]),
                    &#34;relu&#34;,
                    padding,
                ),
                parametric_vgg_block(
                    self.channels[i],
                    self.channels[i],
                    3,
                    deepcopy(normalization_fn[i - 1]),
                    &#34;relu&#34;,
                    padding,
                ),
            )
            self.layers.append(layer)
        self.layers = torch.nn.ModuleList(self.layers)

    def mappings(self):
        mapping = Identity()
        for layer in self.layers[:-1]:
            mapping = mapping + mapping_from_torch_module(layer)
            mapping = mapping + mapping_from_torch_module(self.mp)
        mapping = mapping + mapping_from_torch_module(self.layers[-1])

        return mapping

    def forward(self, images: torch.Tensor) -&gt; torch.Tensor:
        x = images
        for layer in self.layers[:-1]:
            x = layer(x)
            x = self.mp(x)
        x = self.layers[-1](x)
        return x</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li>torch.nn.modules.module.Module</li>
<li><a title="silk.backbones.silk.coords.CoordinateMappingProvider" href="../silk/coords.html#silk.backbones.silk.coords.CoordinateMappingProvider">CoordinateMappingProvider</a></li>
</ul>
<h3>Class variables</h3>
<dl>
<dt id="silk.backbones.superpoint.vgg.ParametricVGG.DEFAULT_NORMALIZATION_FN"><code class="name">var <span class="ident">DEFAULT_NORMALIZATION_FN</span></code></dt>
<dd>
<div class="desc"></div>
</dd>
<dt id="silk.backbones.superpoint.vgg.ParametricVGG.dump_patches"><code class="name">var <span class="ident">dump_patches</span> : bool</code></dt>
<dd>
<div class="desc"></div>
</dd>
<dt id="silk.backbones.superpoint.vgg.ParametricVGG.training"><code class="name">var <span class="ident">training</span> : bool</code></dt>
<dd>
<div class="desc"></div>
</dd>
</dl>
<h3>Methods</h3>
<dl>
<dt id="silk.backbones.superpoint.vgg.ParametricVGG.forward"><code class="name flex">
<span>def <span class="ident">forward</span></span>(<span>self, images: torch.Tensor) ‑> torch.Tensor</span>
</code></dt>
<dd>
<div class="desc"><p>Defines the computation performed at every call.</p>
<p>Should be overridden by all subclasses.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Although the recipe for forward pass needs to be defined within
this function, one should call the :class:<code>Module</code> instance afterwards
instead of this since the former takes care of running the
registered hooks while the latter silently ignores them.</p>
</div></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def forward(self, images: torch.Tensor) -&gt; torch.Tensor:
    x = images
    for layer in self.layers[:-1]:
        x = layer(x)
        x = self.mp(x)
    x = self.layers[-1](x)
    return x</code></pre>
</details>
</dd>
<dt id="silk.backbones.superpoint.vgg.ParametricVGG.mappings"><code class="name flex">
<span>def <span class="ident">mappings</span></span>(<span>self)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def mappings(self):
    mapping = Identity()
    for layer in self.layers[:-1]:
        mapping = mapping + mapping_from_torch_module(layer)
        mapping = mapping + mapping_from_torch_module(self.mp)
    mapping = mapping + mapping_from_torch_module(self.layers[-1])

    return mapping</code></pre>
</details>
</dd>
</dl>
</dd>
<dt id="silk.backbones.superpoint.vgg.VGG"><code class="flex name class">
<span>class <span class="ident">VGG</span></span>
<span>(</span><span>num_channels: int = 1, use_batchnorm: bool = False, use_max_pooling: bool = True, padding: int = 1)</span>
</code></dt>
<dd>
<div class="desc"><p>The VGG backbone.</p>
<p>Initialize the VGG backbone model.
Can take an input image of any number of channels (e.g. grayscale, RGB).</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class VGG(torch.nn.Module, CoordinateMappingProvider):
    &#34;&#34;&#34;
    The VGG backbone.
    &#34;&#34;&#34;

    def __init__(
        self,
        num_channels: int = 1,
        use_batchnorm: bool = False,
        use_max_pooling: bool = True,
        padding: int = 1,
    ):
        &#34;&#34;&#34;
        Initialize the VGG backbone model.
        Can take an input image of any number of channels (e.g. grayscale, RGB).
        &#34;&#34;&#34;
        torch.nn.Module.__init__(self)
        CoordinateMappingProvider.__init__(self)

        assert padding in {0, 1}

        self.padding = padding
        self.use_max_pooling = use_max_pooling

        if use_max_pooling:
            self.mp = torch.nn.MaxPool2d(2, stride=2)
        else:
            self.mp = torch.nn.Identity()

        # convolution layers (encoder)
        self.l1 = torch.nn.Sequential(
            vgg_block(
                num_channels,
                64,
                3,
                use_batchnorm=use_batchnorm,
                padding=padding,
            ),
            vgg_block(
                64,
                64,
                3,
                use_batchnorm=use_batchnorm,
                padding=padding,
            ),
        )

        self.l2 = torch.nn.Sequential(
            vgg_block(
                64,
                64,
                3,
                use_batchnorm=use_batchnorm,
                padding=padding,
            ),
            vgg_block(
                64,
                64,
                3,
                use_batchnorm=use_batchnorm,
                padding=padding,
            ),
        )

        self.l3 = torch.nn.Sequential(
            vgg_block(
                64,
                128,
                3,
                use_batchnorm=use_batchnorm,
                padding=padding,
            ),
            vgg_block(
                128,
                128,
                3,
                use_batchnorm=use_batchnorm,
                padding=padding,
            ),
        )

        self.l4 = torch.nn.Sequential(
            vgg_block(
                128,
                128,
                3,
                use_batchnorm=use_batchnorm,
                padding=padding,
            ),
            vgg_block(
                128,
                128,
                3,
                use_batchnorm=use_batchnorm,
                padding=padding,
            ),
        )

    def mappings(self):
        mapping = Identity()
        mapping = mapping + mapping_from_torch_module(self.l1)
        mapping = mapping + mapping_from_torch_module(self.mp)
        mapping = mapping + mapping_from_torch_module(self.l2)
        mapping = mapping + mapping_from_torch_module(self.mp)
        mapping = mapping + mapping_from_torch_module(self.l3)
        mapping = mapping + mapping_from_torch_module(self.mp)
        mapping = mapping + mapping_from_torch_module(self.l4)

        return mapping

    def forward(self, images: torch.Tensor) -&gt; torch.Tensor:
        &#34;&#34;&#34;
        Goes through the layers of the VGG model as the forward pass.
        Computes the output.
        Args:
            images (tensor): image pytorch tensor with
                shape N x num_channels x H x W
        Returns:
            output (tensor): the output point pytorch tensor with
            shape N x cell_size^2+1 x H/8 x W/8.
        &#34;&#34;&#34;
        o1 = self.l1(images)
        o1 = self.mp(o1)

        o2 = self.l2(o1)
        o2 = self.mp(o2)

        o3 = self.l3(o2)
        o3 = self.mp(o3)

        # features
        o4 = self.l4(o3)

        return o4</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li>torch.nn.modules.module.Module</li>
<li><a title="silk.backbones.silk.coords.CoordinateMappingProvider" href="../silk/coords.html#silk.backbones.silk.coords.CoordinateMappingProvider">CoordinateMappingProvider</a></li>
</ul>
<h3>Class variables</h3>
<dl>
<dt id="silk.backbones.superpoint.vgg.VGG.dump_patches"><code class="name">var <span class="ident">dump_patches</span> : bool</code></dt>
<dd>
<div class="desc"></div>
</dd>
<dt id="silk.backbones.superpoint.vgg.VGG.training"><code class="name">var <span class="ident">training</span> : bool</code></dt>
<dd>
<div class="desc"></div>
</dd>
</dl>
<h3>Methods</h3>
<dl>
<dt id="silk.backbones.superpoint.vgg.VGG.forward"><code class="name flex">
<span>def <span class="ident">forward</span></span>(<span>self, images: torch.Tensor) ‑> torch.Tensor</span>
</code></dt>
<dd>
<div class="desc"><p>Goes through the layers of the VGG model as the forward pass.
Computes the output.</p>
<h2 id="args">Args</h2>
<dl>
<dt><strong><code>images</code></strong> :&ensp;<code>tensor</code></dt>
<dd>image pytorch tensor with
shape N x num_channels x H x W</dd>
</dl>
<h2 id="returns">Returns</h2>
<p>output (tensor): the output point pytorch tensor with
shape N x cell_size^2+1 x H/8 x W/8.</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def forward(self, images: torch.Tensor) -&gt; torch.Tensor:
    &#34;&#34;&#34;
    Goes through the layers of the VGG model as the forward pass.
    Computes the output.
    Args:
        images (tensor): image pytorch tensor with
            shape N x num_channels x H x W
    Returns:
        output (tensor): the output point pytorch tensor with
        shape N x cell_size^2+1 x H/8 x W/8.
    &#34;&#34;&#34;
    o1 = self.l1(images)
    o1 = self.mp(o1)

    o2 = self.l2(o1)
    o2 = self.mp(o2)

    o3 = self.l3(o2)
    o3 = self.mp(o3)

    # features
    o4 = self.l4(o3)

    return o4</code></pre>
</details>
</dd>
<dt id="silk.backbones.superpoint.vgg.VGG.mappings"><code class="name flex">
<span>def <span class="ident">mappings</span></span>(<span>self)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def mappings(self):
    mapping = Identity()
    mapping = mapping + mapping_from_torch_module(self.l1)
    mapping = mapping + mapping_from_torch_module(self.mp)
    mapping = mapping + mapping_from_torch_module(self.l2)
    mapping = mapping + mapping_from_torch_module(self.mp)
    mapping = mapping + mapping_from_torch_module(self.l3)
    mapping = mapping + mapping_from_torch_module(self.mp)
    mapping = mapping + mapping_from_torch_module(self.l4)

    return mapping</code></pre>
</details>
</dd>
</dl>
</dd>
</dl>
</section>
</article>
<nav id="sidebar">
<h1>Index</h1>
<div class="toc">
<ul></ul>
</div>
<ul id="index">
<li><h3>Super-module</h3>
<ul>
<li><code><a title="silk.backbones.superpoint" href="index.html">silk.backbones.superpoint</a></code></li>
</ul>
</li>
<li><h3><a href="#header-functions">Functions</a></h3>
<ul class="">
<li><code><a title="silk.backbones.superpoint.vgg.parametric_vgg_block" href="#silk.backbones.superpoint.vgg.parametric_vgg_block">parametric_vgg_block</a></code></li>
<li><code><a title="silk.backbones.superpoint.vgg.vgg_block" href="#silk.backbones.superpoint.vgg.vgg_block">vgg_block</a></code></li>
</ul>
</li>
<li><h3><a href="#header-classes">Classes</a></h3>
<ul>
<li>
<h4><code><a title="silk.backbones.superpoint.vgg.ParametricVGG" href="#silk.backbones.superpoint.vgg.ParametricVGG">ParametricVGG</a></code></h4>
<ul class="">
<li><code><a title="silk.backbones.superpoint.vgg.ParametricVGG.DEFAULT_NORMALIZATION_FN" href="#silk.backbones.superpoint.vgg.ParametricVGG.DEFAULT_NORMALIZATION_FN">DEFAULT_NORMALIZATION_FN</a></code></li>
<li><code><a title="silk.backbones.superpoint.vgg.ParametricVGG.dump_patches" href="#silk.backbones.superpoint.vgg.ParametricVGG.dump_patches">dump_patches</a></code></li>
<li><code><a title="silk.backbones.superpoint.vgg.ParametricVGG.forward" href="#silk.backbones.superpoint.vgg.ParametricVGG.forward">forward</a></code></li>
<li><code><a title="silk.backbones.superpoint.vgg.ParametricVGG.mappings" href="#silk.backbones.superpoint.vgg.ParametricVGG.mappings">mappings</a></code></li>
<li><code><a title="silk.backbones.superpoint.vgg.ParametricVGG.training" href="#silk.backbones.superpoint.vgg.ParametricVGG.training">training</a></code></li>
</ul>
</li>
<li>
<h4><code><a title="silk.backbones.superpoint.vgg.VGG" href="#silk.backbones.superpoint.vgg.VGG">VGG</a></code></h4>
<ul class="">
<li><code><a title="silk.backbones.superpoint.vgg.VGG.dump_patches" href="#silk.backbones.superpoint.vgg.VGG.dump_patches">dump_patches</a></code></li>
<li><code><a title="silk.backbones.superpoint.vgg.VGG.forward" href="#silk.backbones.superpoint.vgg.VGG.forward">forward</a></code></li>
<li><code><a title="silk.backbones.superpoint.vgg.VGG.mappings" href="#silk.backbones.superpoint.vgg.VGG.mappings">mappings</a></code></li>
<li><code><a title="silk.backbones.superpoint.vgg.VGG.training" href="#silk.backbones.superpoint.vgg.VGG.training">training</a></code></li>
</ul>
</li>
</ul>
</li>
</ul>
</nav>
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